Abstract Much of human history has been dictated by the invisible force of pathogens and infections remain a leading cause of ailment, mortality, and loss of food source or livelihood. In part, this is because our understanding of infection is stymied by the significant variability that exists in patient/host response to infection due to the patient?s unique genetic makeup, as well as the variability in pathogen genomes. This variability determines susceptibility, duration of infection, treatment options, and often, future response to pathogens. Controlled tests to understand the sources of these variabilities cannot be performed on humans due to ethical and practical reasons. An in vitro model is therefore needed that will allow precise control/ tuning of different factors influencing infection outcome. In this proposal, we aim to build a set of droplet microfluidic and molecular biology tools that will allow us to address the issue of variability in infection outcome. Knowing the sources of variability and their genetic underpinnings will help fight infection better, improve patient outcome, reduce cost of care and formulate better treatment strategies in future. We will use custom droplet microfluidics, time-lapse imaging and droplet sorting to: Aim 1: Encapsulate single pathogen cell with single host cell in order to isolate, infect, and image their interaction over time in a representative micro-environment recreated in drops. We will use human macrophage cells as host and Candida albicans for pathogen, as model systems. Host and pathogen cells will be labelled with fluorescent reporters. Fluorescence and cell morphology will be used to determine infection outcome; Aim 2: Use microfluidics to sort infection droplets into two groups based on interaction outcome: Group A, where host cell has successfully overcome pathogen, with pathogen cell undergoing lysis, and Group B, where the pathogen persists resulting in the host cell?s death. We will perform bulk RNA-seq on each group to identify host genes responsible for successful infection suppression in Group A and pathogen genes that likely cause increased virulence in Group B; Aim 3: Build microfluidic and molecular biology tools to profile host-pathogen transcriptomics at single-cell resolution to characterize infection outcome at systems level. The workflow is customizable, modular, and collectively our proposed platform may be used to profile infection in other host or pathogen species at superior control, statistical resolution (~105 host-pathogen pairs), and at low cost (~$0.1/pair).